{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Prophet\n",
    "\n",
    "https://github.com/facebook/prophet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-26T08:52:28.462776Z",
     "start_time": "2021-01-26T08:52:27.571035Z"
    }
   },
   "outputs": [],
   "source": [
    "# Python\n",
    "import pandas as pd\n",
    "from fbprophet import Prophet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-26T08:53:01.312255Z",
     "start_time": "2021-01-26T08:53:00.634423Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ds</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2007-12-10</td>\n",
       "      <td>9.590761</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2007-12-11</td>\n",
       "      <td>8.519590</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2007-12-12</td>\n",
       "      <td>8.183677</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2007-12-13</td>\n",
       "      <td>8.072467</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2007-12-14</td>\n",
       "      <td>7.893572</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           ds         y\n",
       "0  2007-12-10  9.590761\n",
       "1  2007-12-11  8.519590\n",
       "2  2007-12-12  8.183677\n",
       "3  2007-12-13  8.072467\n",
       "4  2007-12-14  7.893572"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('http://mirror.coggle.club/prophet/example_wp_log_peyton_manning.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-26T08:53:14.856944Z",
     "start_time": "2021-01-26T08:53:12.320446Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:fbprophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<fbprophet.forecaster.Prophet at 0x7f9cd3630898>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m = Prophet()\n",
    "m.fit(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-26T08:53:22.389213Z",
     "start_time": "2021-01-26T08:53:22.196856Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ds</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3265</th>\n",
       "      <td>2017-01-15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3266</th>\n",
       "      <td>2017-01-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3267</th>\n",
       "      <td>2017-01-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3268</th>\n",
       "      <td>2017-01-18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3269</th>\n",
       "      <td>2017-01-19</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             ds\n",
       "3265 2017-01-15\n",
       "3266 2017-01-16\n",
       "3267 2017-01-17\n",
       "3268 2017-01-18\n",
       "3269 2017-01-19"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "future = m.make_future_dataframe(periods=365)\n",
    "future.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-26T08:53:36.317855Z",
     "start_time": "2021-01-26T08:53:32.883987Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ds</th>\n",
       "      <th>yhat</th>\n",
       "      <th>yhat_lower</th>\n",
       "      <th>yhat_upper</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3265</th>\n",
       "      <td>2017-01-15</td>\n",
       "      <td>8.200924</td>\n",
       "      <td>7.488568</td>\n",
       "      <td>8.934262</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3266</th>\n",
       "      <td>2017-01-16</td>\n",
       "      <td>8.525932</td>\n",
       "      <td>7.809271</td>\n",
       "      <td>9.245340</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3267</th>\n",
       "      <td>2017-01-17</td>\n",
       "      <td>8.313330</td>\n",
       "      <td>7.627418</td>\n",
       "      <td>9.012617</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3268</th>\n",
       "      <td>2017-01-18</td>\n",
       "      <td>8.145923</td>\n",
       "      <td>7.466100</td>\n",
       "      <td>8.895998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3269</th>\n",
       "      <td>2017-01-19</td>\n",
       "      <td>8.157831</td>\n",
       "      <td>7.465402</td>\n",
       "      <td>8.833143</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             ds      yhat  yhat_lower  yhat_upper\n",
       "3265 2017-01-15  8.200924    7.488568    8.934262\n",
       "3266 2017-01-16  8.525932    7.809271    9.245340\n",
       "3267 2017-01-17  8.313330    7.627418    9.012617\n",
       "3268 2017-01-18  8.145923    7.466100    8.895998\n",
       "3269 2017-01-19  8.157831    7.465402    8.833143"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "forecast = m.predict(future)\n",
    "forecast[['ds', 'yhat', 'yhat_lower', 'yhat_upper']].tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-26T08:54:16.292424Z",
     "start_time": "2021-01-26T08:54:15.955824Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%pylab inline\n",
    "fig1 = m.plot(forecast)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-26T09:28:21.370347Z",
     "start_time": "2021-01-26T09:28:20.858414Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Will not apply HSTS. The HSTS database must be a regular and non-world-writable file.\n",
      "ERROR: could not open HSTS store at '/home/lyz/.wget-hsts'. HSTS will be disabled.\n",
      "--2021-01-26 17:28:20--  http://mirror.coggle.club/lp1.data.txt\n",
      "正在解析主机 mirror.coggle.club (mirror.coggle.club)... 139.215.131.222\n",
      "正在连接 mirror.coggle.club (mirror.coggle.club)|139.215.131.222|:80... 已连接。\n",
      "已发出 HTTP 请求，正在等待回应... 200 OK\n",
      "长度： 27345 (27K) [text/plain]\n",
      "正在保存至: “lp1.data.txt”\n",
      "\n",
      "lp1.data.txt        100%[===================>]  26.70K  --.-KB/s    用时 0.04s   \n",
      "\n",
      "2021-01-26 17:28:21 (661 KB/s) - 已保存 “lp1.data.txt” [27345/27345])\n",
      "\n"
     ]
    }
   ],
   "source": [
    "!wget http://mirror.coggle.club/lp1.data.txt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# tsfresh\n",
    "https://tsfresh.readthedocs.io/en/latest/index.html\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-26T09:28:37.148472Z",
     "start_time": "2021-01-26T09:28:37.132473Z"
    }
   },
   "outputs": [],
   "source": [
    "from tsfresh.examples.robot_execution_failures import download_robot_execution_failures, \\\n",
    "    load_robot_execution_failures\n",
    "timeseries, y = load_robot_execution_failures(file_name=\"./lp1.data.txt\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-26T12:30:35.966084Z",
     "start_time": "2021-01-26T12:30:35.956554Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>time</th>\n",
       "      <th>F_x</th>\n",
       "      <th>F_y</th>\n",
       "      <th>F_z</th>\n",
       "      <th>T_x</th>\n",
       "      <th>T_y</th>\n",
       "      <th>T_z</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>63</td>\n",
       "      <td>-3</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>62</td>\n",
       "      <td>-3</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>61</td>\n",
       "      <td>-3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>63</td>\n",
       "      <td>-2</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>63</td>\n",
       "      <td>-3</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>63</td>\n",
       "      <td>-3</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>63</td>\n",
       "      <td>-3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>63</td>\n",
       "      <td>-3</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>63</td>\n",
       "      <td>-3</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>61</td>\n",
       "      <td>-3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>61</td>\n",
       "      <td>-3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>64</td>\n",
       "      <td>-3</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>64</td>\n",
       "      <td>-3</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>60</td>\n",
       "      <td>-3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1</td>\n",
       "      <td>14</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>64</td>\n",
       "      <td>-2</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    id  time  F_x  F_y  F_z  T_x  T_y  T_z\n",
       "0    1     0   -1   -1   63   -3   -1    0\n",
       "1    1     1    0    0   62   -3   -1    0\n",
       "2    1     2   -1   -1   61   -3    0    0\n",
       "3    1     3   -1   -1   63   -2   -1    0\n",
       "4    1     4   -1   -1   63   -3   -1    0\n",
       "5    1     5   -1   -1   63   -3   -1    0\n",
       "6    1     6   -1   -1   63   -3    0    0\n",
       "7    1     7   -1   -1   63   -3   -1    0\n",
       "8    1     8   -1   -1   63   -3   -1    0\n",
       "9    1     9   -1   -1   61   -3    0    0\n",
       "10   1    10   -1   -1   61   -3    0    0\n",
       "11   1    11   -1   -1   64   -3   -1    0\n",
       "12   1    12   -1   -1   64   -3   -1    0\n",
       "13   1    13   -1   -1   60   -3    0    0\n",
       "14   1    14   -1    0   64   -2   -1    0"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "timeseries[timeseries['id'] == 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-26T09:29:13.850152Z",
     "start_time": "2021-01-26T09:29:13.039084Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 8 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "timeseries[timeseries['id'] == 3].plot(subplots=True, sharex=True, figsize=(10,10))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-26T09:29:41.057861Z",
     "start_time": "2021-01-26T09:29:37.118228Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Feature Extraction: 100%|██████████| 30/30 [00:03<00:00,  8.81it/s]\n"
     ]
    }
   ],
   "source": [
    "from tsfresh import extract_features\n",
    "extracted_features = extract_features(timeseries, column_id=\"id\", column_sort=\"time\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-26T09:29:50.404406Z",
     "start_time": "2021-01-26T09:29:48.535758Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/lyz/.local/lib/python3.6/site-packages/tsfresh/utilities/dataframe_functions.py:172: RuntimeWarning:\n",
      "\n",
      "The columns ['F_x__partial_autocorrelation__lag_7'\n",
      " 'F_x__partial_autocorrelation__lag_8'\n",
      " 'F_x__partial_autocorrelation__lag_9' ...\n",
      " 'T_z__agg_linear_trend__attr_\"stderr\"__chunk_len_50__f_agg_\"min\"'\n",
      " 'T_z__agg_linear_trend__attr_\"stderr\"__chunk_len_50__f_agg_\"mean\"'\n",
      " 'T_z__agg_linear_trend__attr_\"stderr\"__chunk_len_50__f_agg_\"var\"'] did not have any finite values. Filling with zeros.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from tsfresh import select_features\n",
    "from tsfresh.utilities.dataframe_functions import impute\n",
    "\n",
    "impute(extracted_features)\n",
    "features_filtered = select_features(extracted_features, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-26T09:30:30.293139Z",
     "start_time": "2021-01-26T09:30:25.399087Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Feature Extraction: 100%|██████████| 30/30 [00:02<00:00, 11.29it/s]\n"
     ]
    }
   ],
   "source": [
    "from tsfresh import extract_relevant_features\n",
    "\n",
    "features_filtered_direct = extract_relevant_features(timeseries, y,\n",
    "                                                     column_id='id', column_sort='time')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-26T09:30:31.273407Z",
     "start_time": "2021-01-26T09:30:31.248303Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>F_x__value_count__value_-1</th>\n",
       "      <th>F_x__abs_energy</th>\n",
       "      <th>F_x__range_count__max_1__min_-1</th>\n",
       "      <th>F_y__abs_energy</th>\n",
       "      <th>T_y__variance</th>\n",
       "      <th>T_y__standard_deviation</th>\n",
       "      <th>F_x__fft_coefficient__attr_\"abs\"__coeff_1</th>\n",
       "      <th>T_y__fft_coefficient__attr_\"abs\"__coeff_1</th>\n",
       "      <th>T_y__abs_energy</th>\n",
       "      <th>F_z__standard_deviation</th>\n",
       "      <th>...</th>\n",
       "      <th>T_x__change_quantiles__f_agg_\"var\"__isabs_True__qh_0.2__ql_0.0</th>\n",
       "      <th>F_z__change_quantiles__f_agg_\"mean\"__isabs_True__qh_1.0__ql_0.8</th>\n",
       "      <th>T_x__quantile__q_0.1</th>\n",
       "      <th>F_y__has_duplicate_max</th>\n",
       "      <th>F_y__cwt_coefficients__coeff_13__w_2__widths_(2, 5, 10, 20)</th>\n",
       "      <th>F_y__cwt_coefficients__coeff_14__w_5__widths_(2, 5, 10, 20)</th>\n",
       "      <th>T_y__quantile__q_0.1</th>\n",
       "      <th>F_z__time_reversal_asymmetry_statistic__lag_1</th>\n",
       "      <th>F_x__quantile__q_0.2</th>\n",
       "      <th>F_y__quantile__q_0.7</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>14.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>0.222222</td>\n",
       "      <td>0.471405</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.165352</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1.203698</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.310265</td>\n",
       "      <td>-0.751682</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-5.960000e+02</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>4.222222</td>\n",
       "      <td>2.054805</td>\n",
       "      <td>0.624118</td>\n",
       "      <td>6.020261</td>\n",
       "      <td>90.0</td>\n",
       "      <td>4.333846</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-9.2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.202951</td>\n",
       "      <td>0.057818</td>\n",
       "      <td>-3.6</td>\n",
       "      <td>-6.803846e+02</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>3.128889</td>\n",
       "      <td>1.768867</td>\n",
       "      <td>2.203858</td>\n",
       "      <td>8.235442</td>\n",
       "      <td>103.0</td>\n",
       "      <td>4.616877</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.0</td>\n",
       "      <td>-6.6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.539121</td>\n",
       "      <td>0.912474</td>\n",
       "      <td>-4.0</td>\n",
       "      <td>-6.170000e+02</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>7.128889</td>\n",
       "      <td>2.669998</td>\n",
       "      <td>0.844394</td>\n",
       "      <td>12.067855</td>\n",
       "      <td>124.0</td>\n",
       "      <td>3.833188</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-9.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-2.641390</td>\n",
       "      <td>-0.609735</td>\n",
       "      <td>-4.6</td>\n",
       "      <td>3.426308e+03</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>9.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>4.160000</td>\n",
       "      <td>2.039608</td>\n",
       "      <td>2.730599</td>\n",
       "      <td>6.445330</td>\n",
       "      <td>180.0</td>\n",
       "      <td>4.841487</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-9.6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.591927</td>\n",
       "      <td>0.072771</td>\n",
       "      <td>-5.0</td>\n",
       "      <td>-2.609000e+03</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>0.0</td>\n",
       "      <td>96833.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>42780.0</td>\n",
       "      <td>1563.528889</td>\n",
       "      <td>39.541483</td>\n",
       "      <td>359.248162</td>\n",
       "      <td>309.190088</td>\n",
       "      <td>171261.0</td>\n",
       "      <td>291.988082</td>\n",
       "      <td>...</td>\n",
       "      <td>64.000000</td>\n",
       "      <td>46.0</td>\n",
       "      <td>203.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>38.559593</td>\n",
       "      <td>71.641254</td>\n",
       "      <td>36.4</td>\n",
       "      <td>-7.700628e+07</td>\n",
       "      <td>-105.0</td>\n",
       "      <td>66.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1683.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1523.0</td>\n",
       "      <td>14.755556</td>\n",
       "      <td>3.841296</td>\n",
       "      <td>36.770027</td>\n",
       "      <td>26.631007</td>\n",
       "      <td>503.0</td>\n",
       "      <td>14.501494</td>\n",
       "      <td>...</td>\n",
       "      <td>4.666667</td>\n",
       "      <td>4.5</td>\n",
       "      <td>-41.6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>14.429645</td>\n",
       "      <td>16.349699</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-1.050785e+04</td>\n",
       "      <td>5.8</td>\n",
       "      <td>10.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>0.0</td>\n",
       "      <td>83497.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>21064.0</td>\n",
       "      <td>2788.595556</td>\n",
       "      <td>52.807154</td>\n",
       "      <td>312.044052</td>\n",
       "      <td>429.697740</td>\n",
       "      <td>118013.0</td>\n",
       "      <td>121.420189</td>\n",
       "      <td>...</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>7.0</td>\n",
       "      <td>-84.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>60.760842</td>\n",
       "      <td>71.095480</td>\n",
       "      <td>19.6</td>\n",
       "      <td>-5.544922e+06</td>\n",
       "      <td>30.4</td>\n",
       "      <td>38.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1405437.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>308658.0</td>\n",
       "      <td>6415.715556</td>\n",
       "      <td>80.098162</td>\n",
       "      <td>481.046930</td>\n",
       "      <td>683.196535</td>\n",
       "      <td>2430295.0</td>\n",
       "      <td>204.966621</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>90.5</td>\n",
       "      <td>-139.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>109.029954</td>\n",
       "      <td>173.699573</td>\n",
       "      <td>272.6</td>\n",
       "      <td>-9.881845e+07</td>\n",
       "      <td>246.8</td>\n",
       "      <td>154.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1427.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>113.0</td>\n",
       "      <td>6.906667</td>\n",
       "      <td>2.628054</td>\n",
       "      <td>15.524355</td>\n",
       "      <td>16.768541</td>\n",
       "      <td>7630.0</td>\n",
       "      <td>10.627010</td>\n",
       "      <td>...</td>\n",
       "      <td>4.666667</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-24.6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.455593</td>\n",
       "      <td>3.226801</td>\n",
       "      <td>-26.2</td>\n",
       "      <td>-1.340477e+04</td>\n",
       "      <td>-11.2</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>88 rows × 671 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    F_x__value_count__value_-1  F_x__abs_energy  \\\n",
       "1                         14.0             14.0   \n",
       "2                          7.0             25.0   \n",
       "3                         11.0             12.0   \n",
       "4                          5.0             16.0   \n",
       "5                          9.0             17.0   \n",
       "..                         ...              ...   \n",
       "84                         0.0          96833.0   \n",
       "85                         0.0           1683.0   \n",
       "86                         0.0          83497.0   \n",
       "87                         0.0        1405437.0   \n",
       "88                         0.0           1427.0   \n",
       "\n",
       "    F_x__range_count__max_1__min_-1  F_y__abs_energy  T_y__variance  \\\n",
       "1                              15.0             13.0       0.222222   \n",
       "2                              13.0             76.0       4.222222   \n",
       "3                              14.0             40.0       3.128889   \n",
       "4                              10.0             60.0       7.128889   \n",
       "5                              13.0             46.0       4.160000   \n",
       "..                              ...              ...            ...   \n",
       "84                              0.0          42780.0    1563.528889   \n",
       "85                              0.0           1523.0      14.755556   \n",
       "86                              0.0          21064.0    2788.595556   \n",
       "87                              0.0         308658.0    6415.715556   \n",
       "88                              0.0            113.0       6.906667   \n",
       "\n",
       "    T_y__standard_deviation  F_x__fft_coefficient__attr_\"abs\"__coeff_1  \\\n",
       "1                  0.471405                                   1.000000   \n",
       "2                  2.054805                                   0.624118   \n",
       "3                  1.768867                                   2.203858   \n",
       "4                  2.669998                                   0.844394   \n",
       "5                  2.039608                                   2.730599   \n",
       "..                      ...                                        ...   \n",
       "84                39.541483                                 359.248162   \n",
       "85                 3.841296                                  36.770027   \n",
       "86                52.807154                                 312.044052   \n",
       "87                80.098162                                 481.046930   \n",
       "88                 2.628054                                  15.524355   \n",
       "\n",
       "    T_y__fft_coefficient__attr_\"abs\"__coeff_1  T_y__abs_energy  \\\n",
       "1                                    1.165352             10.0   \n",
       "2                                    6.020261             90.0   \n",
       "3                                    8.235442            103.0   \n",
       "4                                   12.067855            124.0   \n",
       "5                                    6.445330            180.0   \n",
       "..                                        ...              ...   \n",
       "84                                 309.190088         171261.0   \n",
       "85                                  26.631007            503.0   \n",
       "86                                 429.697740         118013.0   \n",
       "87                                 683.196535        2430295.0   \n",
       "88                                  16.768541           7630.0   \n",
       "\n",
       "    F_z__standard_deviation  ...  \\\n",
       "1                  1.203698  ...   \n",
       "2                  4.333846  ...   \n",
       "3                  4.616877  ...   \n",
       "4                  3.833188  ...   \n",
       "5                  4.841487  ...   \n",
       "..                      ...  ...   \n",
       "84               291.988082  ...   \n",
       "85                14.501494  ...   \n",
       "86               121.420189  ...   \n",
       "87               204.966621  ...   \n",
       "88                10.627010  ...   \n",
       "\n",
       "    T_x__change_quantiles__f_agg_\"var\"__isabs_True__qh_0.2__ql_0.0  \\\n",
       "1                                            0.000000                \n",
       "2                                            0.000000                \n",
       "3                                            0.000000                \n",
       "4                                            0.000000                \n",
       "5                                            0.000000                \n",
       "..                                                ...                \n",
       "84                                          64.000000                \n",
       "85                                           4.666667                \n",
       "86                                           0.250000                \n",
       "87                                           0.000000                \n",
       "88                                           4.666667                \n",
       "\n",
       "    F_z__change_quantiles__f_agg_\"mean\"__isabs_True__qh_1.0__ql_0.8  \\\n",
       "1                                                 0.0                 \n",
       "2                                                 1.0                 \n",
       "3                                                 3.0                 \n",
       "4                                                 0.0                 \n",
       "5                                                 0.0                 \n",
       "..                                                ...                 \n",
       "84                                               46.0                 \n",
       "85                                                4.5                 \n",
       "86                                                7.0                 \n",
       "87                                               90.5                 \n",
       "88                                                0.0                 \n",
       "\n",
       "    T_x__quantile__q_0.1  F_y__has_duplicate_max  \\\n",
       "1                   -3.0                     1.0   \n",
       "2                   -9.2                     1.0   \n",
       "3                   -6.6                     0.0   \n",
       "4                   -9.0                     0.0   \n",
       "5                   -9.6                     0.0   \n",
       "..                   ...                     ...   \n",
       "84                 203.2                     0.0   \n",
       "85                 -41.6                     0.0   \n",
       "86                 -84.8                     0.0   \n",
       "87                -139.2                     0.0   \n",
       "88                 -24.6                     0.0   \n",
       "\n",
       "    F_y__cwt_coefficients__coeff_13__w_2__widths_(2, 5, 10, 20)  \\\n",
       "1                                           -0.310265             \n",
       "2                                           -0.202951             \n",
       "3                                            0.539121             \n",
       "4                                           -2.641390             \n",
       "5                                            0.591927             \n",
       "..                                                ...             \n",
       "84                                          38.559593             \n",
       "85                                          14.429645             \n",
       "86                                          60.760842             \n",
       "87                                         109.029954             \n",
       "88                                           2.455593             \n",
       "\n",
       "    F_y__cwt_coefficients__coeff_14__w_5__widths_(2, 5, 10, 20)  \\\n",
       "1                                           -0.751682             \n",
       "2                                            0.057818             \n",
       "3                                            0.912474             \n",
       "4                                           -0.609735             \n",
       "5                                            0.072771             \n",
       "..                                                ...             \n",
       "84                                          71.641254             \n",
       "85                                          16.349699             \n",
       "86                                          71.095480             \n",
       "87                                         173.699573             \n",
       "88                                           3.226801             \n",
       "\n",
       "    T_y__quantile__q_0.1  F_z__time_reversal_asymmetry_statistic__lag_1  \\\n",
       "1                   -1.0                                  -5.960000e+02   \n",
       "2                   -3.6                                  -6.803846e+02   \n",
       "3                   -4.0                                  -6.170000e+02   \n",
       "4                   -4.6                                   3.426308e+03   \n",
       "5                   -5.0                                  -2.609000e+03   \n",
       "..                   ...                                            ...   \n",
       "84                  36.4                                  -7.700628e+07   \n",
       "85                   1.0                                  -1.050785e+04   \n",
       "86                  19.6                                  -5.544922e+06   \n",
       "87                 272.6                                  -9.881845e+07   \n",
       "88                 -26.2                                  -1.340477e+04   \n",
       "\n",
       "    F_x__quantile__q_0.2  F_y__quantile__q_0.7  \n",
       "1                   -1.0                  -1.0  \n",
       "2                   -1.0                  -1.0  \n",
       "3                   -1.0                   0.0  \n",
       "4                   -1.0                   1.0  \n",
       "5                   -1.0                   0.8  \n",
       "..                   ...                   ...  \n",
       "84                -105.0                  66.8  \n",
       "85                   5.8                  10.6  \n",
       "86                  30.4                  38.4  \n",
       "87                 246.8                 154.8  \n",
       "88                 -11.2                   3.0  \n",
       "\n",
       "[88 rows x 671 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features_filtered_direct"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# pyts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-26T09:39:20.469537Z",
     "start_time": "2021-01-26T09:39:20.245128Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(50, 150) (50,)\n",
      "0.98\n",
      "0.9533333333333334\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/lyz/.local/lib/python3.6/site-packages/pyts/bag_of_words/bow.py:246: FutureWarning:\n",
      "\n",
      "BagOfWords has been reworked in 0.11 in order to match its definition in the literature. To get the old BagOfWords, use pyts.bag_of_words.WordExtractor instead.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from pyts.classification import *\n",
    "from pyts.datasets import load_gunpoint\n",
    "X_train, X_test, y_train, y_test = load_gunpoint(return_X_y=True)\n",
    "print(X_train.shape, y_train.shape)\n",
    "\n",
    "clf = BOSSVS(window_size=28)\n",
    "clf.fit(X_train, y_train)\n",
    "print(clf.score(X_test, y_test))\n",
    "\n",
    "clf = SAXVSM()\n",
    "clf.fit(X_train, y_train)\n",
    "print(clf.score(X_test, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-26T09:44:45.349831Z",
     "start_time": "2021-01-26T09:44:29.270459Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/lyz/.local/lib/python3.6/site-packages/sklearn/utils/validation.py:70: FutureWarning:\n",
      "\n",
      "Pass classes=[1 2], y=[2 2 1 1 2 2 2 2 2 1 1 1 1 1 2 1 2 2 1 2 1 1 1 2 1 2 1 1 2 1 1 2 2 1 2 1 2\n",
      " 2 2 2 2 1 1 1 2 2 1 2 1 2] as keyword args. From version 0.25 passing these as positional arguments will result in an error\n",
      "\n",
      "/home/lyz/.local/lib/python3.6/site-packages/numpy/core/_asarray.py:83: VisibleDeprecationWarning:\n",
      "\n",
      "Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from pyts.utils import windowed_view\n",
    "\n",
    "# Load the data set and fit the classifier\n",
    "X, _, y, _ = load_gunpoint(return_X_y=True)\n",
    "clf = LearningShapelets(random_state=42, tol=0.01)\n",
    "clf.fit(X, y)\n",
    "\n",
    "# Select two shapelets\n",
    "shapelets = np.asarray([clf.shapelets_[0, -9], clf.shapelets_[0, -12]])\n",
    "\n",
    "# Derive the distances between the time series and the shapelets\n",
    "shapelet_size = shapelets.shape[1]\n",
    "X_window = windowed_view(X, window_size=shapelet_size, window_step=1)\n",
    "X_dist = np.mean(\n",
    "    (X_window[:, :, None] - shapelets[None, :]) ** 2, axis=3).min(axis=1)\n",
    "\n",
    "plt.figure(figsize=(14, 4))\n",
    "\n",
    "# Plot the two shapelets\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.plot(shapelets[0])\n",
    "plt.plot(shapelets[1])\n",
    "plt.title('Two learned shapelets', fontsize=14)\n",
    "\n",
    "# Plot the distances\n",
    "plt.subplot(1, 2, 2)\n",
    "for color, label in zip('br', (1, 2)):\n",
    "    plt.scatter(X_dist[y == label, 0], X_dist[y == label, 1],\n",
    "                c=color, label='Class {}'.format(label))\n",
    "plt.title('Distances between the time series and both shapelets',\n",
    "          fontsize=14)\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-26T09:45:31.431422Z",
     "start_time": "2021-01-26T09:45:31.403777Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/lyz/.local/lib/python3.6/site-packages/pyts/bag_of_words/bow.py:246: FutureWarning:\n",
      "\n",
      "BagOfWords has been reworked in 0.11 in order to match its definition in the literature. To get the old BagOfWords, use pyts.bag_of_words.WordExtractor instead.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from pyts.bag_of_words import BagOfWords\n",
    "X, _, _, _ = load_gunpoint(return_X_y=True)\n",
    "window_size, word_size = 30, 5\n",
    "bow = BagOfWords(window_size=window_size, word_size=word_size,\n",
    "                 window_step=window_size, numerosity_reduction=False)\n",
    "X_bow = bow.transform(X)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-26T09:45:34.539743Z",
     "start_time": "2021-01-26T09:45:34.534643Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['ddaaa bbbbd acccc ddbaa acdcc', 'abbdd bbbbd acccb dcbaa abccd',\n",
       "       'bdbac abbcd cddba dcbaa dbabb', 'abbbd aabcd cccca dcbaa dabcc',\n",
       "       'abcdd aabcd cccca cabcc ddbaa', 'dcbaa aabcd cccca dcabb ddbaa',\n",
       "       'bbbbd acccc ccdca cccba dabbb', 'bbbbd acccc cdcaa ccaad cccca',\n",
       "       'bbbcd dccaa cccca dcbaa bcabd', 'aabdd aacdd dcabb ddcaa ddcaa',\n",
       "       'aacdd aacdd cccca dcbaa dabcc', 'aabdd aacdd dccaa ddcaa ddbaa',\n",
       "       'babbd aabdd ddbaa cccca dbbbb', 'abbcd aacdd dcabb ddcaa ddcaa',\n",
       "       'abcdc bbbbd acccc dcaab acdca', 'aabdd aacdd ddcba ddcaa ddcaa',\n",
       "       'abcdd aabcd acccc ddbaa cbabd', 'ddbba bbbbd acccc dcbaa acdcb',\n",
       "       'aaadd aacdd aabcd ddcaa ddaaa', 'bbbbd acdcb dbabd ddbaa bdcba',\n",
       "       'acccc abbbd acccc ddcaa ddaab', 'cdbac aacdd ddcba ddbaa ddaab',\n",
       "       'abbcd aacdd dcbca ddcaa ddcba', 'bbbbd acccc ddbaa dccba ddcba',\n",
       "       'abbbd aabcd cccca dbbbb aacdd', 'bbbcd dccaa ddcba dbbbb dcbaa',\n",
       "       'aaadd aacdd dcbaa ddcaa ddcaa', 'aabdd aacdd baadd ddcaa ddcaa',\n",
       "       'aaacd ddbba dbbca ddbaa abcdc', 'abbcd aabdd acdcc dccba dcbaa',\n",
       "       'aabdd aacdd cccca dbbba abcdd', 'bbbbd acccc aabcd cccca dcbaa',\n",
       "       'bbbbd abccd cccca dcabb acccd', 'ddbaa abbcd bccca dcbaa dbbbb',\n",
       "       'abcdd aabcd bcdca dbabb baacd', 'ddcaa aabcd cccca dbbbb aacdd',\n",
       "       'bbbbd acccc cccca cdcba dbbbb', 'dcbba bbbbd acdca dcabb abbcd',\n",
       "       'bbbbd acccc acacd caccb ddcaa', 'bbbbd acccc dcbbb ddcba abcdd',\n",
       "       'acccc aabcd acdca dcbaa adcbc', 'aaadd aabdd abcdc ddcba dccba',\n",
       "       'aabdd aacdd cccca dcbaa dabbb', 'abbcd aacdd dcbaa ddbaa ddcaa',\n",
       "       'bbbcd babcd ddcba ddbaa bddba', 'cdcba aabcd cccca dbabb dcbaa',\n",
       "       'abadd aaacd cccca dbbbb dbabb', 'aacdd bbbbd acccb dcaab acccd',\n",
       "       'aabcd aabcd cccca dbabb dabcc', 'bbbbd abccc dbcac cccca dbabb'],\n",
       "      dtype='<U29')"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_bow"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# sktime\n",
    "\n",
    "https://github.com/alan-turing-institute/sktime\n",
    "\n",
    "https://github.com/alan-turing-institute/sktime/blob/master/examples/02_classification_univariate.ipynb\n",
    "\n",
    "https://github.com/sktime/sktime-dl/blob/master/examples/univariate_time_series_classification.ipynb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-26T09:53:18.452810Z",
     "start_time": "2021-01-26T09:52:42.772896Z"
    }
   },
   "outputs": [],
   "source": [
    "from sktime.classification.all import *\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "X, y = load_arrow_head(return_X_y=True)\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y)\n",
    "classifier = TimeSeriesForest()\n",
    "classifier.fit(X_train, y_train)\n",
    "y_pred = classifier.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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